import torch
import torch.nn as nn
from .._internally_replaced_utils import load_state_dict_from_url
from typing import Any

__all__ = ['AlexNet', 'alexnet']

model_urls = {
    'alexnet': 'https://download.pytorch.org/models/alexnet-owt-7be5be79.pth',
}


class AlexNet(nn.Module):
    def __init__(self, num_classes: int=1000) -> None:
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(
                3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(
                kernel_size=3, stride=2),
            nn.Conv2d(
                64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(
                kernel_size=3, stride=2),
            nn.Conv2d(
                192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(
                384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(
                256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(
                kernel_size=3, stride=2), )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes), )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


def alexnet(pretrained: bool=False, progress: bool=True,
            **kwargs: Any) -> AlexNet:
    r"""AlexNet model architecture from the
    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
    The required minimum input size of the model is 63x63.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = AlexNet(**kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls['alexnet'], progress=progress)
        model.load_state_dict(state_dict)
    return model
